1. Field of the Invention
This invention relates generally to the maximization of manufacturing profits and, more particularly, to a method and system for simultaneous price optimization and asset allocation to maximize manufacturing profits.
2. Description of the Related Art
In the domains of travel/transportation, hospitality, retail, stores and companies are in business to sell merchandise and other products to make a profit. In the retail context, store managers are most concerned with product-related marketing and decisions such as product placement, assortment, space, price, promotion, and inventory reduction. If the products are non-optimized in terms of these product decisions, then sales can be lost, costs could be higher, and profit will be less than what would otherwise be possible in an optimal system. For example, if the price is too high or too low, profit can be lost. If promotions are not properly targeted, then marketing efforts will be wasted. In order to maximize the outcome of product related decisions, many of these domains have used statistical modeling and strategic planning to optimize the decision making process for each of the product decisions. The objective for price optimization has been pricing the product for existing inventory reduction. These solutions find optimal prices for everyday/dynamic pricing, pricing for promotions, and pricing for markdowns (e.g. discontinued products). Current price optimization solutions deal with only finished goods and the depletion of an existing picture of inventory of the finished goods.
Thus, existing price optimization solutions do not deal with the unique challenges in the manufacturing industry. In manufacturing, the traditional picture of inventory includes accounting for replenishment. However, price optimizations have failed to consider optimal inventory allocation of both finished goods as well as their component parts, and replenishment of both finished goods as well as their component parts.
Although, revenue management solutions for the travel/transportation, hospitality, retail, and Consumer Packaged Goods (CPG) domains consider optimal inventory allocation of finished goods, these solutions do not consider allocation of component parts or replenishment of finished goods inventories and component part inventories. Moreover, these solution fail to consider the supply chain of the finished goods and the impact of capacity and other constraints present in the supply chain.
Supply chain planning solutions generally implement order fulfillment using what the industry refers to as a “supply plan.” A supply plan is a tactical tool which has a plan horizon, generally of the order of a few weeks. The supply plan combines current product supply and projections of customer demand with supply-side constraints (for example, material requirements, capacity requirements, product manufacturing, and assembly times) to determine future planned supplies. These supplies are then output to near-term execution level systems which use supply plans to fulfill customer requests. Such execution system are often referred to as available-to-promise (ATP) systems where available-to-promise refers to the ability to promise product availability based on a predefined statement of current and planned supply and capacity (the supply plan). Using a supply plan, ATP systems are able to associate product quantities with the dates such products are scheduled to be available for shipment. ATP systems use this information to promise delivery of the products to customers by specific dates. Although the supply plans consider optimal inventory allocation of both finished goods and component parts, supply chain management fails to perform price optimization. Moreover, supply plans typically do not consider price to be variable.
Existing solutions for price-revenue-supply chain optimizations have significant shortcomings. One approach performs price optimization separately without consideration of the inventory. Once the optimal prices are determined, the prices may be fed into a supply chain module to produce recommendations for inventory levels. However, this feasible but sub-optimal solution does not provide an accurate model of the problem, failing to consider inventory replenishment or supply-side constraints. This solution does not capture multi-item interaction between the profit, price, demand, satisfiable demand and supply-side constraints, and the uncertainty and non-linearity in the demand. Another feasible, but sub-optimal approach performs price optimization for a set of finished goods taking into consideration only the on-hand inventory of finished goods and its depletion. Other, more advanced approaches consider finished goods inventory replenishment as well, but ignore component inventory replenishment and capacity constraints. There are numerous limitations to these approaches. First, all replenishment decisions are taken only at the beginning of the plan, rather than through-out the planning horizon. Second, inventory is not allowed to be carried over from one time period to another. Third, limitations are imposed on the form of the demand model. For example, the demand is assumed to be a linear function of the price and cross-item effects are ignored. Thus the ideal solution should consider the price selection and inventory allocation problems simultaneously in the objective function, together with a supply chain model, which may include component replenishment, inventory carryover, and capacity constraints, and an accurate demand model.
Each industry's price optimization and supply chain has a unique set of challenges. In the manufacturing sector, the predominant focus has been on minimizing cost. Essentially, cost is a driving factor for many of manufacturing-related decisions. Price optimization solutions including asset allocation have not been considered in the manufacturing domain.